Bachelorarbeit, 2022
43 Seiten, Note: 78.00
ACKNOWLEDGEMENT
CHAPTER ONE: INTRODUCTION
1.1 Background
1.2 Statement of the problem.
1.3 Significance of the study
1.4 Objectives
1.4.1 General objective
1.4.2 Specific objectives
CHAPTER TWO: LITERATURE REVIEW
2.1 Theoretical review
2.1.1 Smith's gap theory
2.1.2 Information asymmetry theory
2.1.3 Maximum housing expenditure theory
2.1.4 The residual income approach
2.1.5 Heterogeneity theory
2.2 Conceptual framework
2.3.1 Type of house and its influence on rental price
2.3.2 Housing demand and its influence on rental growth
2.3.3 Structural characteristics and their influence on rental prices
2.3.4 Household income and its relation to rental prices
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Overview
3.2 Research Design
3.3 Target population and sampling
3.4 Data collection methods and instruments
3.4.1 Source of data
3.4.2 Collection of data
3.5 Data management
3.6 Ethical consideration
3.7 Data analysis strategies
CHAPTER FOUR: DATA ANALYSIS, PRESENTATION, INTERPRETATION, AND DISCUSSION
4.1 Introduction
4.2 Descriptive statistics
4.2.1 Distribution of households by the place of residence
4.2.2 Distribution of type of the house
4.2.3 Average rent by type of house.
4.2.5 Status of the floor
4.2.6 Main wall Material
4.3 Inferential Statistics.
4.3.1 Multiple linear regression analysis
4.3.2 Model summary
4.3.3 Coefficients
CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS
5.1 Introduction
5.2 Discussions from findings obtained
5.3 Conclusion
5.4 Recommendations
5.5 Challenges for the study
5.6 Suggestions for further research
REFERENCES
Table 1.1: place of residence
Table 1.2: Distribution of type of the house
Table 1.3: Average rent
Table 1.4: status of the floor
Table 1.5: Main wall material
Table 1.6: Model summary
Figure 1.1: Distribution by Type of house
Figure 1.2: Distribution by Type of house
Figure 1.3: Average rent by type of house
Figure 1.4: Average rent by type of house
Figure 1.5: Types of house by location
Figure 1.6: Status of floor on the type of houses
Figure 1.7: Main wall material on houses
Housing, or more general living spaces, is a fundamental aspect of existence, giving shelter, protection, warmth, and a place to rest for human beings.
This study aimed at analyzing changes in rental price with particular emphasis on Voi town and its environs. In our study, we wished to determine the common types of houses rented in Voi, the amount paid as rent and the key determinants of house rent in Voi.
Structured questionnaires executed on a Kobo-Collect mobile data collection platform was used to gather data. A sample size of 384 households was obtained from 23 locations within Voi and its environs. The data was analyzed using both qualitative and quantitative methods. A Multiple linear regression model was employed to identify the determinant of house rent prices in Voi and its environs. Our dependent variable (House rent price) and our independent variables (Household income, Type of house).
We are thankful to the Almighty God for His mercy and grace. His will has once again manifested, and all glory belongs to Him. We acknowledge our supervisors, Dr. Oscar Ngesa and Madam Rachael Kyalo of Taita Taveta University, who contributed towards the writing of this research project. Special thanks are due to our parents for financial support from the beginning of our study; without their contribution, neither the course nor the research project would have been possible. We also register our appreciation to the entire Taita Taveta University administration for the course offered.
Globally, the primary goal of economic development is to ensure that every country's population has access to suitable and affordable housing. Variations of rental houses occur between one urban area to another as a result of population density, socioeconomic status, and location just to mention a few. According to Hochstenbach (2019), since the second world war, cities in the West have experienced waves of urbanization and counter-urbanization, which has led to the establishment of residential rental property on a massive scale to facilitate accommodation of folks living there.
Saghir and Santoro (2018) acknowledge that in the next 30 years, the population of developing nations is expected to rise from 11.3% in 2010 to 20.2% by 2050, with an annual increase of 65million urban residents. Further, Skeldon (2017) projects that over one million people are born in or relocate to cities every week.
Africa is experiencing rapid urban growth where hundreds of millions of tenants live in the urban areas of Africa, although we are uncertain about the precise numbers. In various African urban centers, newly arrived migrants, students, and those who have changed jobs or divorced rely on rental accommodation as a temporary solution (Güneralp et al., 2017). As a result, to accommodate this increasing urban population, more housing units need to be constructed each year in Africa Although Africa is the least urbanized continent, it is the one that is rapidly urbanizing and faces a significant housing shortage.
Through the African Union (AU), African countries have put extra effort into adopting Agenda 2063 to transform Africa better in 50 years. This agenda prioritizes seven aspirations, one of them being prosperous Africa, based inclusive growth and sustainable development having one among its goals to provide modern and livable habitats and basic quality services; which are among the main challenges facing African cities due to the high rate of urbanization. AU aims to mostly work in line with the Sustainable Development Goals (SDG11) to provide modern and livable habitats and high-quality essential services by making the cities and settlements secure, resilient, and viable (Kimaro & Fourie, 2017).
Kenya, being one of the countries in Africa, has a tremendous rural-to-urban migration, with Nairobi, Mombasa, and Kisumu counties accounting for the majority of this movement; thus, the demand for housing is very high. This situation has caused the current scenario in Nairobi where the low-income population outnumbers the supply of affordable housing leading to a rapid rise in the number of slum-dwellers. Although there is a significant deficit in the housing availability for low-income earners in these cities, there is a large supply of expensive housing for the accommodation of middle and high-class people. According to the Ministry of Land, Housing, and Urban Development, Kenya's housing demand is predicted to be 200,000 units yearly, but only 30,000 to 40,000 are delivered each year (Chege, 2017).
To curb this housing challenge, through President Uhuru Kenyatta, the government in 2017 initiated the big four agenda with affordable housing being one of the four pillars. In partnership with the private sector, the government allocated $183 million to construct 500,000 affordable homes over a five-year period which involves a number of incentives and supports to enable the delivery of affordable housing (Obillo, 2020; Muigua, 2019).
The other urban centers in Kenya are currently experiencing a housing challenge mainly characterized by a few decent houses than the population they are accommodating. As a result, slums emerge, mostly known as "Ghettos", where we have houses of low rents. These areas are occupied by tenants from all over the country due to the availability of goods and services at fair prices to meet their needs in towns.
In line with the above background, we assessed the housing rent dynamics in Voi town and pinpointed the factors affecting them and how they relate with the rent prices in different places encompassing Voi.
Housing, a basic need in an individual's life, is a key component that constitutes shelter. This thus makes affordable housing a necessity. The population growth rate in Africa, especially in Kenya, is extremely rapid and demands that affordable houses should be constructed. Rental housing is the solution to a country with a very high population due to the scarcity of land brought by the intense land fragmentation
Urban centres are characterized by the availability of job opportunities and social amenities. Economic activities are higher in urban areas than rural areas; thus, resources are readily available, and people have easy access to most essential services. These are some of the factors that drive most people to move to urban areas. People move from rural areas with very high hopes and expectations that their lives will assume a positive paradigm shift, hoping that the city will cater to all their needs.
According to a report by the World Health Organization, 39.1% of the Sub-Saharan population live in urban areas (Alkire & Housseini, 2014). It is also projected that the population of city dwellers in these Sub-Saharan countries will double by 2030. However urbanization is linked to poverty, especially in African countries. Most people move to the urban centres with hopes of securing good and well-paying jobs that will later turn their lives around, although that has not been the case since most of them usually end up leading poor lives in slums. People opt to live in such places because of the high cost of living in the cities since the majority cannot afford to pay rent or even own a small piece of land.
The current Kenyan government administration under his Excellency honorable Uhuru Muigai Kenyatta is very focused on enhancing the livelihoods of all its citizens. In this regard, the government established the big 4 agenda listing housing as one of the four. Affordable housing is every individual's dream. Homelessness is ranked high among the challenges any government aims at bringing down. To curb this challenge, the government through the Affordable Housing Program (AHP) is providing a number of incentives and supports to enable the delivery of affordable housing, focusing on homeownership.
This study therefore seeks to investigate the housing rent dynamics of Voi town and its environs. We purpose to interact with residents encompassing Voi town. We will seek to understand these housing rent dynamics as well as investigate factors that influence people's choice of houses in this locality. We are also seeking to understand why some houses have higher rent than others.
The primary purpose of our study was to come up with a statistical model that helped us understand the factors that influenced the rents of the houses in Voi town and its environs.
The findings in this study contributed to a better understanding of rent prices and their impacts on household choices depending on their structure, location, tenant's income, and nearness to social amenities.
The information will help:
i. In policy-making in relation to rental housing
ii. Households in making informed decisions and choices about their housing needs
iii. Private and government developers in developing models for rental housing.
To analyze variations in rental prices in Voi town and its environs.
1. Determine average rent for different types of houses in Voi and its environs
2. To determine the common types of houses (single room, bedsitter, one-bedroom, 2+ bedroom) by location in Voi
3. To identify determinants of house rent in Voi.
Neil Smith (1979) discovered a theory that talks about the concept of gentrification process. The theory talks about the concept of the gentrification process. The process describes or points out the disparity between the current rental prices and the affordable rental income. Neil Smith used two approaches: a highly empirical approach that stresses the collection of descriptive information about cases of gentrification and occasionally the results of other studies (Maloutas, 2012). A theoretical approach was also used where the causes of gentrification were explained. A number of researchers have stressed the need to bridge the gap between empirical and theoretical approaches. According to Wu et al. (2017), gentrification presents major theoretical ideas with case studies, summarizes the ideas in the book, and avails ideas for future research.
This theory was designed after a study was conducted to investigate how rent and vacancy rates were affected by information asymmetry. Data from the Hong Kong office was analyzed. The theory states that there is a negative correlation between the information asymmetry, that is, if it is known and the rent adjustment is in response to external shocks. Information asymmetry makes it harder for a consensus to be attained between the landlord and the tenant on matters of rent. The theory also states that information asymmetry is less serious when it comes to a high-end office unit because most of the value of rent is obtained from it is easily observational by the landlord and the tenant (Yang et al., 2021).
This theory stipulates that the household's location is determined by the availability of income and conditions of mortgage finance (Sexton et al., 2012). Their option is moderately confined, and the option on the housing price will be dependent largely on the availability of mortgage.
The theory assumes that house buyers will try to secure houses they can afford and the location of their own choice.
This theory is mainly concerned with housing affordability. It was invented by Michael Etone and Terry Broke. The approach focused on what a household can afford on shelter after considering the other necessary payments. The theory was discovered after another similar research was conducted in Australia that is 25% rule (Meen, 2018). We are assessing the overall affordability in the housing market.
Urban housing units vary so much in many dimensions that households' choice greatly demonstrates the essential elements of consumer choice. The taste for different housing features and components differs for every household; for instance, households can be sensitive about the house structure to the details such as architectural designs, fittings, condition (whether newly constructed), and the like. Therefore, they have carryout an extensive valuation and search for their perfect or rather suitable unit. The difference in consumer tastes is what we term as heterogeneity. However, housing heterogeneity is distinct as the demand for the housing unit may also be affected by external forces. A good example of these external forces is the neighborhood and location factors.
The neighborhood factor can be viewed in 2 different dimensions. One the supplier for housing units may have no control of their immediate neighbor. For instance, if the housing units neighbors factories or industries producing loads of unmanaged dump waste or gases, it may heavily limit the occupancy of the housing units. In addition, the households may choose to dwell in a place because of the neighborhood. Comparatively, if a household has good and friendly neighbors and environment, it may overshadow other factors affecting their stay in a housing unit.
The whole nature of a conceptual framework in a research study is to help redirect efforts and channeling of the ideas so that the research can be a success. The word conceptual comes from the concept, which means an idea or a notion, while the word framework comes from the word frame, which means the whole skeleton or frame of the research is to be discussed. In our pursuit to have a complete understanding of the idea of housing rent dynamics. We are going to explore some theories and concepts that will give us a clear and bigger picture of the topic under study
Independent Variables
-Household income
-Type of house
Dependent variable
-Housing rent price
The dynamics in housing rent and prices are greatly affected by the type of house. Houses are characterized by a wide range of types that is we have the single rooms, the bedsitters, the one- bedrooms, the two bedrooms etc. and both have different type of rent prices. Nowadays, in many macro enterprises, one of the many incentives for hiring employees and motivating them is availing a residential area ( Koktashev et al., 2019).
The choice of a house pricing largely depends on the number of rooms meaning that the single rooms are cheaper than the one-bedrooms and the bedsitters. A research on the hedonic pricing model applied to housing markets was conducted to estimate the price of a house in Vietnam. The study found that the price of a house is heavily affected by the type of house, number of rooms, number of amenities, and size of the house. (Nguyen et al., 2020).
According to Badawy (2019), the cost of buying or renting a house in Kenya is very high, i.e., the average price of a one-bedroom house in Kenya in 2018 was at Ksh. 55,313/=, a two-bedroom house was at Ksh. 89,220 and a three-bedroom was at Ksh. 90,348. Type of house and its price is directly affecting the affordability, i.e., it is evident from the above statistics that the affordability of a house is affected by its type.
KNBS (2015) describes middle-class households as those individuals whose monthly income ranges between KES 23671 to KES 112717 (Badawy, 2019). This shows that most people in Kenya are on the middleclass margin and opt for renting houses, mainly the bedsitters, one- bedroom, and two-bedroom houses.
Housing prices eventually increased by more than 100% in 2015 from 2004 (Cheruiyot, 2015). This thus makes housing unaffordable to a majority of citizens who are mainly middle class. Therefore, the housing and rental markets are greatly affected by the type of house as the different houses have different rents and prices.
As the population increase, the demand for housing also increases. According to Makena (2012), the main factors influencing housing demand in Nairobi County are income, price of housing, and the number of households. Factors such as interest rate and inflation rate do not affect the demand for housing in Nairobi.
A research was conducted in Nakuru East-sub County whereby descriptive research study design was used and found that housing demand was influenced by rental growth income (M'nyiri, 2018). Therefore, the researchers advised the government to lower the price and the tax on constructing materials which will reduce the cost for marketing residential rental property.
A linear model for housing price, average lending rates, income per capita, cost of other related goods, and inflation of housing units purchased was established using the time series data from 1980-2009. It was then concluded that the income per capita was the factor that influences the demand for housing, mainly in Kenya and other non-housing goods had no effect on the demand for housing (Mutwiwa, 2017).
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